Several Aspects of Pruning Methods in Recursive Least Square Algorithms for Neural Networks
نویسندگان
چکیده
Recently, recursive least square (RLS), or extended Kalman ltering (EKF), based algorithms have been demonstrated to be a class of eeective online training methods for neural networks. This paper discusses several aspects of pruning a neural network trained by the RLS based approach. Based on our study, the RLS approach is implicitly a weight decay training algorithm. Also, we derive two pruning methods for RLS based algorithms by making use of their by-products. Finally, we will present a new RLS algorithm which can improve the generalization of neural networks.
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